3 citations · 5 across the 12 of their papers we have counts for
13 papers
Dense Clinical Contrasts Enhance Medical Knowledge Updating in Large Language Models
Yangmin Huang, Shu Quan, He Geng +5
Medical knowledge changes continually, making large language models vulnerable to relying on outdated yet clinically plausible information. We study whether the format of supervisi…
ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training
Rongsheng Wang, Fenghe Tang, Zihang Jiang +10
Learning transferable and interpretable representations from medical volumetric scans remains challenging due to complex anatomical structures and weak, heterogeneous supervision p…
ProMedical: Hierarchical Fine-Grained Criteria Modeling for Medical LLM Alignment via Explicit Injection
He Geng, Yangmin Huang, Lixian Lai +5
Aligning Large Language Models (LLMs) with high-stakes medical standards remains a significant challenge, primarily due to the dissonance between coarse-grained preference signals…
SimCroP: Radiograph Representation Learning with Similarity-driven Cross-granularity Pre-training
Rongsheng Wang, Fenghe Tang, Qingsong Yao +8
Medical vision-language pre-training shows great potential in learning representative features from massive paired radiographs and reports. However, in computed tomography (CT) sca…
MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts
Jiayi He, Yangmin Huang, Qianyun Du +5
Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory compliance. We introduce MedFact, a chall…
Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster
Fenghe Tang, Wenxin Ma, Zhiyang He +3
With the advancement of Large Language Model (LLM) for natural language processing, this paper presents an intriguing finding: a frozen pre-trained LLM layer can process visual tok…
MVP-CBM:Multi-layer Visual Preference-enhanced Concept Bottleneck Model for Explainable Medical Image Classification
Chunjiang Wang, Kun Zhang, Yandong Liu +3
The concept bottleneck model (CBM), as a technique improving interpretability via linking predictions to human-understandable concepts, makes high-risk and life-critical medical im…
EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records
Shuguang Zhao, Qiangzhong Feng, Zhiyang He +8
Medical consultation dialogues contain critical clinical information, yet their unstructured nature hinders effective utilization in diagnosis and treatment. Traditional methods, r…
Bridged Semantic Alignment for Zero-shot 3D Medical Image Diagnosis
Haoran Lai, Zihang Jiang, Qingsong Yao +6
3D medical images such as computed tomography are widely used in clinical practice, offering a great potential for automatic diagnosis. Supervised learning-based approaches have ac…
E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model
Haoran Lai, Zihang Jiang, Qingsong Yao +6
The development of 3D medical vision-language models holds significant potential for disease diagnosis and patient treatment. However, compared to 2D medical images, 3D medical ima…
CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification
Haoran Lai, Qingsong Yao, Zihang Jiang +4
The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs, focusing on image-text alignment. H…
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-training
Rongsheng Wang, Qingsong Yao, Zihang Jiang +4
Despite significant advancements in medical vision-language pre-training, existing methods have largely overlooked the inherent linguistic complexity and imbalanced isssue within m…
Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray
Haoran Lai, Qingsong Yao, Zhiyang He +2
Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. However, current computer-aided diagnosis (CAD) methods focus on common diseases, leading to inadequate…